Scholarlink

This investigation aimed to determine the optimal parameters for Abrasive Water Jet Machining (AWJM) of Monel 400 alloys. To enhance key performance metrics of Surface Roughness (SR) and Material Removal Rate (MRR), critical AWJM process variables namely Transverse Rate (TR), Stand-off Distance (SD), and Abrasive Flow Rate (FR) were considered. Various metaheuristic algorithms, such as Multiverse Optimization (MVO), Antlion Optimization (ALO), Grey Wolf Optimization (GWO), and the Grasshopper Algorithm (GHO), were employed to optimize the machining parameters. To obtain Pareto-optimal solutions, these metaheuristic techniques were integrated with Multi-Criteria Decision-Making (MCDM) methods, including TOPSIS, Deng’s method and EDAS, using the Hypervolume (HV) indicator. Experimental validation demonstrated an error margin of less than 3% between predicted and actual results, confirming the effectiveness of the proposed optimization framework. The optimal AWJM machining parameters of TR = 250 mm/min, SD = 1 mm, and FR = 238 g/min were identified using the GWO-EDAS approach, yielding an MRR of 0.449 g/s and SR of 2.43 μm.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Comparative Approach to Multi-response Optimization for AWJM of Monel 400 Alloys Using Hybrid Metaheuristic-MCDM Techniques

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Ayyappan Solaiyappan,
  • Sivakumar Mahalingam

摘要

This investigation aimed to determine the optimal parameters for Abrasive Water Jet Machining (AWJM) of Monel 400 alloys. To enhance key performance metrics of Surface Roughness (SR) and Material Removal Rate (MRR), critical AWJM process variables namely Transverse Rate (TR), Stand-off Distance (SD), and Abrasive Flow Rate (FR) were considered. Various metaheuristic algorithms, such as Multiverse Optimization (MVO), Antlion Optimization (ALO), Grey Wolf Optimization (GWO), and the Grasshopper Algorithm (GHO), were employed to optimize the machining parameters. To obtain Pareto-optimal solutions, these metaheuristic techniques were integrated with Multi-Criteria Decision-Making (MCDM) methods, including TOPSIS, Deng’s method and EDAS, using the Hypervolume (HV) indicator. Experimental validation demonstrated an error margin of less than 3% between predicted and actual results, confirming the effectiveness of the proposed optimization framework. The optimal AWJM machining parameters of TR = 250 mm/min, SD = 1 mm, and FR = 238 g/min were identified using the GWO-EDAS approach, yielding an MRR of 0.449 g/s and SR of 2.43 μm.